import math

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms

from raw.base_model import BaseModel

training_params = {
    'num_epochs': 200,
    'batch_size': 32,
    'learning_rate': 1e-3
}

def conv3x3(in_planes, out_planes, stride = 1):
    # 3x3 convolution with padding
    return nn.Conv2d(in_planes, out_planes, 3, stride = stride, padding = 1, bias = False)

class BasicBlock(nn.Module):
    def __init__(self, inplanes, planes, stride = 1, downsample = None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace = True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out

class Model(BaseModel):
    def __init__(self, options):
        super().__init__('CIFAR10', training_params)

        input_channels = 3 * options.get('thermometer_level', 1)

        self.inplanes = 16
        self.conv1 = nn.Conv2d(input_channels, 16, 3, stride = 1, padding = 1, bias = True)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace = True)
        self.layer1 = self._make_layer(BasicBlock, 16, 3)
        self.layer2 = self._make_layer(BasicBlock, 32, 3, stride = 2)
        self.layer3 = self._make_layer(BasicBlock, 64, 3, stride = 2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(64, 10)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride = 1):
        downsample = None
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes, 1, stride = stride, bias = True),
                nn.BatchNorm2d(planes)
            )

        layers = list([])
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        x = self.avg_pool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        x = x - torch.max(x, dim = 1, keepdim = True)[0]
        return x

    def get_optimizer(self):
        return optim.Adam(self.parameters(), lr = training_params['learning_rate'])

    def adjust_optimizer(self, opt, epoch):
        minimum_learning_rate = 0.5e-6
        for param_group in opt.param_groups:
            lr_temp = param_group["lr"]
            if epoch == 80 or epoch == 120 or epoch == 160:
                lr_temp = lr_temp * 1e-1
            elif epoch == 180:
                lr_temp = lr_temp * 5e-1
            param_group["lr"] = max(lr_temp, minimum_learning_rate)
            print('The learning rate is set to {}'.format(param_group["lr"]))

    def get_train_loader_transform(self):
        return transforms.Compose([
            transforms.RandomAffine(degrees = 0, translate = (0.1, 0.1)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor()
        ])
